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Ashraf Yehia El-Naggar

Researcher at Taif University

Publications -  45
Citations -  281

Ashraf Yehia El-Naggar is an academic researcher from Taif University. The author has contributed to research in topics: Silica gel & Natural gas. The author has an hindex of 8, co-authored 45 publications receiving 224 citations. Previous affiliations of Ashraf Yehia El-Naggar include Egyptian Petroleum Research Institute.

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Journal Article

Hydrocarbon degrading bacteria as indicator of petroleum pollution in Ismailia Canal, Egypt.

TL;DR: In this article, water and sediment samples from four sites extending for about 20 Km from the beginning of Ismailia canal were collected seasonally for the chemical screening of hydrocarbon pollutants via capillary gas chromatography and collected monthly for the bacterial assessment during the period from August 2007 to July 2008.
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API gravities, vanadium, nickel, sulfur, and their relation to gross composition: Implications for the origin and maturation of crude oils in Western Desert, Egypt

TL;DR: In this paper, the geochemical analyses of API gravities, vanadium, nickel, sulfur, and bulk composition were performed on eight samples from productive wells in Gindi, South Deep Abu-gharadig, Abu-GHARADig, Dahab-Merier, and Faghure basins locates in the North Western Desert.
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Surface-modified silica gels as solid stationary phases in gas chromatography

TL;DR: In this article, surface textural characteristics were investigated from nitrogen adsorption-desorption isotherms at −196 °C and the surface modified silica gels were tested as GC solid stationary phases in terms of the separation efficiency for various groups of non-polar and polar solutes.
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Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

TL;DR: In this paper, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network.
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Silica, alumina and aluminosilicates as solid stationary phases in gas chromatography

TL;DR: In this article, the parent and thermally treated materials were characterized by means of FTIR, SEM and thermal analysis (DTA and TGA) in order to elucidate the main structural properties.